Creating Powerful and Interpretable Models with Regression Networks

Lachlan O'Neill, Simon Angus, Satya Borgohain, Nader Chmait and David Dowe

No 2021-09, SoDa Laboratories Working Paper Series from Monash University

Abstract: As the discipline has evolved, research in machine learning has been focused more and more on creating more powerful neural networks, without regard for the interpretability of these networks. Such “black-box models” yield state-of-the-art results, but we cannot understand why they make a particular decision or prediction. Sometimes this is acceptable, but often it is not.

Keywords: machine learning; policy evaluation; neural networks; regression; classification (search for similar items in EconPapers)
JEL-codes: C14 C45 C52 (search for similar items in EconPapers)
Date: 2021-09-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-isf
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Citations: View citations in EconPapers (2)

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